Tracking and Abnormal Behavior Detection in Video Surveillance Using Optical Flow and Neural Networks

2014 ◽  
Author(s):  
Nida Rasheed ◽  
Shoab A. Khan ◽  
Adnan Khalid
Keyword(s):  
Neural Networks ◽  
Optical Flow ◽  
Video Surveillance ◽  
Abnormal Behavior ◽  
Behavior Detection ◽  
Abnormal Behavior Detection

scholarly journals Abnormal Human Behavior Detection in Videos: A Review

2021 ◽  
Vol 50 (3) ◽  
pp. 522-545
Author(s):  
Huiyu Mu ◽  
Ruizhi Sun ◽  
Gang Yuan ◽  
Yun Wang
Keyword(s):  
Video Surveillance ◽  
Human Behavior ◽  
Abnormal Behavior ◽  
Surveillance Systems ◽  
Behavior Detection ◽  
Event Modeling ◽  
Semantic Inference ◽  
Abnormal Behavior Detection ◽  
Private And Public ◽  
New Perspective

Modeling human behavior patterns for detecting the abnormal event has become an important domain in recentyears. A lot of efforts have been made for building smart video surveillance systems with the purpose ofscene analysis and making correct semantic inference from the video moving target. Current approaches havetransferred from rule-based to statistical-based methods with the need of efficient recognition of high-levelactivities. This paper presented not only an update expanding previous related researches, but also a study coveredthe behavior representation and the event modeling. Especially, we provided a new perspective for eventmodeling which divided the methods into the following subcategories: modeling normal event, predictionmodel, query model and deep hybrid model. Finally, we exhibited the available datasets and popular evaluationschemes used for abnormal behavior detection in intelligent video surveillance. More researches will promotethe development of abnormal human behavior detection, e.g. deep generative network, weakly-supervised. It isobviously encouraged and dictated by applications of supervising and monitoring in private and public space.The main purpose of this paper is to widely recognize recent available methods and represent the literature ina way of that brings key challenges into notice.

scholarly journals Abnormal behavior detection based on dense trajectories

2018 ◽  
Vol 42 (3) ◽  
pp. 476-482
Author(s):  
R. A. Shatalin ◽  
V. R. Fidelman ◽  
P. E. Ovchinnikov
Keyword(s):  
Video Surveillance ◽  
Principal Components ◽  
Abnormal Behavior ◽  
Behavior Detection ◽  
Dense Trajectories ◽  
Detection Algorithms ◽  
Bag Of Features ◽  
Displacement Vectors ◽  
Abnormal Behavior Detection ◽  
Surveillance Applications

In this paper, we propose abnormal behavior detection algorithms based on dense trajectories and principal components for video surveillance applications. The result shows that the proposed algorithms are faster than an algorithm based on lengths of displacement vectors but the accuracy is only retained if the bag-of-features model is trained on a balanced sample of behavior features.

scholarly journals Abnormal Behavior Detection Scheme of UAV Using Recurrent Neural Networks

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 110293-110305 ◽  
Author(s):  
Ke Xiao ◽  
Jianyu Zhao ◽  
Yunhua He ◽  
Chaofei Li ◽  
Wei Cheng
Keyword(s):  
Neural Networks ◽  
Recurrent Neural Networks ◽  
Abnormal Behavior ◽  
Detection Scheme ◽  
Behavior Detection ◽  
Abnormal Behavior Detection
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